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Ryan Palo
Ryan Palo

Posted on • Updated on • Originally published at

Each Pair with Itertools

Cover Photo by Dan Gold on Unsplash.

Quick Tip Time!

This week I was working on some coding challenges on Rosalind in Python. For one of the problems, I needed to run through consecutive groups of characters in a string and do something with them. For example, if the string was "ACGTACAGTACTGACAGATCA", I wanted to operate on the following substrings:

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If I was writing in Ruby, this would be a perfect use case for the each_cons method! Unfortunately, no such luck with a similar function built-in to Python. I ended up going with the slightly less pretty:

def each_cons(n, iterable):
    """Returns every n consecutive items in an iterable.
    Example: each_cons(4, range(10)) => [0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5]...
    return (iterable[i: i + n] for i in range(len(iterable) - n + 1))
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Anytime you write range(len(..., there's probably a better way you could have done things. If you know a slicker, less indexy way to do this, please share it, because there's got to be a better way. I can't be indexing into things manually like a barbarian.

However, that's not what this post is about. In my search for a better way, I came across a clever use of the itertools module that was too good not to share.

Each Pair with Itertools

This method only really works cleanly if you need only two consecutive items, but it feels very Pythonic to me. I'll show you the code and then point out the key parts.

from itertools import tee

def each_pair(iterable):
    """Returns each consecutive pair of items in an iterable"""
    first, second = tee(iterable)
    next(second, None)
    return zip(first, second)

# => [0, 1], [1, 2], [2, 3], [3, 4], [4, 5]...
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Ahhhh SO GOOD.

The Magic Explained

So, how does it work?

First, we use itertools.tee, which returns two copies of the original iterable (a fancy word for anything you can iterate through: strings, lists, generators, etc.). It also takes an optional argument for n if you want to make n copies instead of just 2.

Now we have two identical copies of the original iterable. We then call next on the second one in order to step it one item forward. Notice the second argument to next, None. The next function returns (and uses up) the next item in an iterable, and if you provide it a default and it can't return another item because that iterable is empty, it just returns the default.

# next only works on Iterator objects
a = iter([1, 2, 3])
next(a, "EMPTY")
# => 1
next(a, "EMPTY")
# => 2
next(a, "EMPTY")
# => 3
next(a, "EMPTY")
# => "EMPTY"
# => Error!  StopIteration!
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This is just a neat way of saying "cycle the second iterable one item forward, but if you're already empty, don't worry about it. Just don't throw an error." You probably also noticed that we don't do anything with the result of this call to next. We're just throwing an item away, if it exists.

The last part is zipping the two iterables together. zip is a tricky function that seems simple, but often the results can be hard to wrap your head around. It makes each n-th item of each iterator it is passed into one single list.

It's easier to see in an example:

a = [1, 2, 3, 4]
b = ["Apple", "Banana", "Pear", "Soup"]
c = [True, False, True, False]

zip(a, b, c)
# => [
#        [1, "Apple", True],
#        [2, "Banana", False],
#        [3, "Pear", True],
#        [4, "Soup", False],
# ]
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The trick is that, if our lists have different lengths, it will only use up to the shortest one.

a = [1, 2, 3, 4, 5, 6, 7, 8]
b = [1, 2, 3]
zip(a, b)
# => [[1, 1], [2, 2], [3, 3]]  See how it drops the last 5 elements of 'a'?
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The itertools module also has a zip_longest function that takes a default value to fill in if some of the arguments to zip are too short.

a = [1, 2, 3, 4, 5, 6]
b = [1, 2]
itertools.zip_longest(a, b, fillvalue=":)")
# => [
#     [1, 1],
#     [2, 2],
#     [3, ":)"]
#     [4, ":)"]
#     [5, ":)"]
#     [6, ":)"]
# ]

Wrap Up

Anyways, I thought this was neat. I like it anytime I can get what I want out of an iterable without actually using any brute force indices. It seems like it cuts down on off-by-one errors.

Like I said before, if you've got a better way to make the each_cons function above, I want to see it. Be sure to comment or tweet it at me.

Originally posted on assert_not magic?

Top comments (1)

somacdivad profile image
David Amos • Edited

For your each_cons function, try this out:

def each_cons(n, iterable):
    iters = [iter(iterable)] * n
    return itertools.zip_longest(*iters)

That will get you an iterator over groups of n consecutive elements in iterable. No indexing or list building required!

This is basically the grouper function that can be found in the recipes section of the itertools docs.

You can also find it (and other examples of itertools goodness) in my write-up at

EDIT: I just looked at your requirements for each_cons again, and realized my function above isn't quite what you want.

For an example of what you're looking for, check out the windowed() function in the more-itertools package.

It's not built-in to Python, but you can look at the source. A bit more verbose than your solution, but for large iterables it's the way to go. Uses far less memory and will be way faster than using list slices.